Diabetes technology & therapeutics · 2009
Estimation of future glucose concentrations with subject-specific recursive linear models.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Eren-Oruklu Meriyan, Cinar Ali, Quinn Lauretta, Smith Donald
Аннотация
BACKGROUND: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data.
METHODS: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA).
RESULTS: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more.
CONCLUSIONS: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.
METHODS: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA).
RESULTS: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more.
CONCLUSIONS: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.